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Merged notebook changes from release 1.0.45

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Heather Shapiro
2019-06-26 14:39:09 -04:00
parent 4a6bcebccc
commit 249bcac3c7
74 changed files with 11362 additions and 6761 deletions
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73
how-to-use-azureml/explain-model/explain-tabular-data-raw-features/explain-sklearn-raw-features.ipynb
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@@ -36,22 +36,6 @@
"4. Visualize the global and local explanations with the visualization dashboard."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This example needs sklearn-pandas. If it is not installed, uncomment and run the following line."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#!pip install sklearn-pandas"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -63,7 +47,6 @@
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
"from sklearn.linear_model import LogisticRegression\n",
"from azureml.explain.model.tabular_explainer import TabularExplainer\n",
"from sklearn_pandas import DataFrameMapper\n",
"import pandas as pd\n",
"import numpy as np"
]
@@ -113,6 +96,13 @@
"x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"sklearn imports"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -121,7 +111,51 @@
"source": [
"from sklearn.pipeline import Pipeline\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.compose import ColumnTransformer\n",
"\n",
"transformations = ColumnTransformer([\n",
" (\"age_fare\", Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='median')),\n",
" ('scaler', StandardScaler())\n",
" ]), [\"age\", \"fare\"]),\n",
" (\"embarked\", Pipeline(steps=[\n",
" (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
" (\"encoder\", OneHotEncoder(sparse=False))]), [\"embarked\"]),\n",
" (\"sex_pclass\", OneHotEncoder(sparse=False), [\"sex\", \"pclass\"]) \n",
"])\n",
"\n",
"\n",
"# Append classifier to preprocessing pipeline.\n",
"# Now we have a full prediction pipeline.\n",
"clf = Pipeline(steps=[('preprocessor', transformations),\n",
" ('classifier', LogisticRegression(solver='lbfgs'))])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"# Uncomment below if sklearn-pandas is not installed\n",
"#!pip install sklearn-pandas\n",
"from sklearn_pandas import DataFrameMapper\n",
"\n",
"# Impute, standardize the numeric features and one-hot encode the categorical features. \n",
@@ -141,7 +175,8 @@
"# Append classifier to preprocessing pipeline.\n",
"# Now we have a full prediction pipeline.\n",
"clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),\n",
" ('classifier', LogisticRegression(solver='lbfgs'))])"
" ('classifier', LogisticRegression(solver='lbfgs'))])\n",
"'''"
]
},
{